From going gaga over Labubus and bidding adieu to Stranger Things to experimenting on Nano Banana, culture, tech, and human ingenuity kept crossing wires over 2025. And nothing summed it up better than Collins Dictionary declaring “vibe coding” the word of the year. It served as a reminder that even in a world of autonomous agents, reasoning engines, and self-optimizing pipelines, the breakthroughs that actually move the needle come from people who follow a hunch, probe the edges, and dare to try the unorthodox because that’s where innovation begins.
This curiosity has always been the beat we march to at Tiger Analytics. Being “learn-it-alls” isn’t a mindset our #TigerTribe adopted this year. It’s the engine behind every model we built, every system we optimized, and every impact we delivered since we got started 14 years ago. In 2025, it meant building agentic CX platforms that orchestrated millions of customer interactions in real time, designing economically intelligent personalization frameworks that turned individual digital twins into measurable lift, deploying dynamic promotion engines that adapted to hyper-local demand with minimal manual oversight; along with many other breakthroughs driven by the same curiosity.
Our year-end edition of AI of the Tiger is a rewind of what happens when relentless experimentation, technical rigour and domain expertise meet a culture that refuses to stop learning – It’s how you build a team that defines what comes next.
Chapter 1: Build Around the User
We’ve said it before, and we’re saying it again – integrated solutions, platforms and products are what drive scalability, and usability for businesses and users. When we focus on the user and what they want to accomplish, we build solutions that are relevant, technically sound, and more likely to be adopted. We applied this product-thinking mindset in our work with a leading consumer goods multinational, moving from simply addressing manual planogram audits to building an integrated mobile application for store digitization, capable of offline operation, video analytics, smart annotation, and image stitching. This end-to-end solution cut audit times from up to 40 minutes to just 6 and improved accuracy from 60% to 90%. More in our guide.
Chapter 2: Mind Your Models
For us, curiosity manifests in controlled experimentation, understanding the rules of the game to build reliable systems. It’s what we cover in our guide for data leaders on responsible AI. Strong governance isn’t bureaucracy. It’s the foundation that lets AI scale safely. When a leading mobile payment services provider partnered with us to create GenAI-driven financial advisory services, the goal was to ensure the AI stayed within guardrails while delivering trustworthy and accurate advice. We built a Wealth Coach and Copilot that combined sentiment and intent models with LLM outputs orchestrated through Prompt Flow, pre-processed and indexed from historical interactions, SOPs, and financial content. Two interfaces served agents and end customers, translating complex model outputs into actionable guidance. The results: improved financial literacy and investment decisions, faster query resolution, and 88% model accuracy with 100% compliance.
Chapter 3: In Data We Trust
We need models we can trust, and they need data they can rely on. That’s the principle behind our proprietary Snowflake-native data quality framework, where validation, monitoring, and lineage are built directly into the pipelines. The same philosophy guided our work with Pelabuhan Tanjung Pelepas (PTP), where we consolidated 10+ operational sources into a Databricks lakehouse with Unity Catalog, creating a single, governed source of truth. This helped complete real-time reporting, complex calculations in minutes, optimized Prime Mover allocation, and created a foundation ready for ML-driven initiatives like delinquency prediction and power-consumption anomaly detection. When data is engineered for reliability, every model, insight, and decision becomes scalable and trustworthy.
Chapter 4: AI Agent, Reporting for Duty
If LinkedIn had a word of the year, our bet would be on Agentic AI. Yet, the uncomfortable truth is most enterprise AI assistants still behave like enhanced chatbots. While these are helpful, they are far from the multi-step reasoning, orchestration, and autonomous workflow execution we expect from agentic systems. As we explored in our guide on harnessing Agentic AI with Snowflake, the real shift comes from tying intelligence to operational context, letting systems perceive what’s happening, decide what needs to be done, and act across workflows instead of waiting for a prompt. This is exactly what we enabled for a global food & beverage major. Together, we built a GenAI-powered agentic solution that integrated directly with an E2E Order-to-Fulfillment Control Tower, providing real-time visibility, proactive issue resolution, continuous learning, and dynamic workforce optimization across sites. This resulted in improved fulfillment accuracy, fewer delays and stockouts, smarter labor distribution, and a shift from reactive operations to AI-supported decision-making.
Our biggest breakthroughs have always come from our tribe’s curiosity in action, asking the right questions, challenging assumptions, and solving the toughest problems. As we close the chapter on 2025, we’re excited for the experiments, lessons, and discoveries that 2026 has in store.
